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جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱۸۱-۱۹۵ از ۲۲۷ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Satoshi Sekineمشاهده پروفایل

استاد پژوهشی

New York UniversityUnited States

Satoshi Sekine is a Research Professor in the Computer Science Department at New York University (NYU), affiliated with the Proteus Project. His roles include Research Associate Professor since 2007, with prior positions as Assistant Research Professor and Research Scientist at NYU since 1994. He holds a Ph.D. in Computer Science from NYU (1998), an M.Sc. from the University of Manchester Institute of Science and Technology (1992), and a B.Sc. from Tokyo Institute of Technology (1987). His research focuses on Natural Language Processing (NLP), including information extraction, syntactic analysis, and knowledge discovery from corpora. Key projects include On-Demand Information Extraction (ODIE), Extended Named Entity (ENE) systems, and Web People Search (WePS) tasks. He has organized major conferences like HLT-NAACL 2006 and contributed to initiatives such as the IREX evaluation project. Publications span topics like neural entity classification, dependency parsing, and cross-lingual NLP. He has supervised PhD students Kiyoshi Sudo and Yusuke Shinyama, and led collaborations with companies like Fujitsu, NEC, and NHK. Grants include NSF funding for projects like On-Demand Information Extraction ($2M, 2003-2008). He serves as a program chair for ACL and ANLP conferences, reviews for top journals, and advises PhD committees. His work integrates linguistic theories with practical NLP applications, emphasizing evaluation-driven research and cross-lingual systems.

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Benjamim Fonsecaمشاهده پروفایل

استادیار

University of Trás-os-Montes and Alto DouroPortugal

Benjamim Fonseca is an Assistant Professor with Habilitation at the University of Trás-os-Montes and Alto Douro (UTAD), affiliated with the Department of Engineering. He is also a researcher at INESC TEC and co-founder/CEO of 4ALL Software, a UTAD spin-off focused on innovative software development. His research spans collaborative systems, mobile accessibility, and immersive technologies, with over 100 publications in conferences, journals, and books. Fonseca actively contributes to scientific event organization and peer review, emphasizing human-AI collaboration, scientometrics, and educational technology. His work integrates hybrid human-AI systems, crowdsourcing frameworks, and educational tools like virtual labs and exergames. Notable projects include the Online-Gym multiuser virtual environment and SOSPhone , a mobile emergency app for deaf users. Fonseca’s research also explores the ethical and practical dimensions of AI in fields like music composition and entrepreneurship education through platforms like BIZZY and PLAYER . Key themes in his publications include scientometric analysis of CSCW (Computer-Supported Cooperative Work), taxonomies for crowd-machine interaction, and the application of reinforcement learning in scheduling. His interdisciplinary approach bridges computer science, education, and social inclusion, with a focus on leveraging technology for equitable access and collaborative innovation.

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Eugene Wuمشاهده پروفایل

دانشیار

Columbia UniversityUnited States

Eugene Wu is an Associate Professor of Computer Science at Columbia University, affiliated with the Data Science Institute and co-chair of the Center for Data, Media, and Society. His research focuses on interactive data systems, visualization, and human-data interaction. Wu holds a Ph.D. from MIT and B.S. from UC Berkeley, with postdoctoral experience at AMPLab. Education: Ph.D., Computer Science, MIT B.S., Computer Science, University of California, Berkeley Postdoc, AMPLab Research Interests: Eugene's work bridges data systems, visualization, and human-computer interaction. Key areas include: - Interactive data interfaces and visualization tools - Large Language Models (LLMs) for document processing and prompt development - Lineage tracking and explainable systems - Privacy-aware query processing and scalable analytics. His recent projects address task-based dataset search, complaint-driven debugging, and systems for LLM agents. Recent Articles Trends: Publications emphasize LLM integration (e.g., Prompt Editor, Cocoon), scalable systems (FaDE, ConnectorX), and foundational contributions to lineage and visualization (Smoke, Physical Visualization Design). Awards: NSF CAREER Award VLDB 10-Year Test of Time Award (2018) SIGMOD Best Demo Award (2016, 2025) Amazon Research Award Advising & Grants: Advises Ph.D./postdoc students on database and ML systems. Leads NSF-funded projects on entity matching and interactive debugging. Co-organizes SIGMOD panels and workshops on database research directions. Labs & Teams: Leads the WuLab research group and collaborates with industry partners like Celonis and Adobe. Active in the Data Science Institute and Center for Data, Media, and Society.

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Jonas Theinerمشاهده پروفایل

پژوهشگر

German National Library of Science and TechnologyGermany

Jonas Theiner is a Researcher at the Technische Informationsbibliothek (TIB) – German National Library of Science and Technology , affiliated with the Data Science & Digital Libraries division. His work bridges Sports Analytics , Computer Vision , and Deep Learning , focusing on camera calibration for sports field registration and enriching player position data with visual information. Previously, he worked on large-scale visual place recognition from photos. Research Trends : His publications since 2019 demonstrate expertise in: Camera calibration for soccer field registration Monocular depth estimation analysis Visual geolocation of photos Multimodal sports data integration Football formation classification Scientific Recognition : Best Paper Award at ACM International Conference on Multimedia Retrieval (ICMR 2020)

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Roee Shragaمشاهده پروفایل

استادیار

Northeastern UniversityUnited States

Roee Shraga is an Assistant Professor in the Computer Science department and Data Science program at Worcester Polytechnic Institute (WPI). He previously held a Postdoctoral Research Associate position at the Data Lab within Khoury College of Computer Sciences at Northeastern University in Boston, MA. Ph.D. in Data Science, Technion – Israel Institute of Technology (2020) M.E. in Data Science, Technion (2017) B.Sc. in Industrial Engineering and Management (Information Management Engineering), Technion (2015) Shraga’s research focuses on Data Integration , Data Discovery , Human-in-the-loop Machine Learning , Information Retrieval , and Large Language Models (LLMs) . His work bridges data management, machine learning, and human-computer interaction to improve data quality in AI systems. Recent publications include advancements in LLM-driven data integration , semantic table union search , and contrastive entity resolution . Key contributions include the Explain-Da-V framework for semantic data versioning and FlexER for intent-aware entity resolution. Scientific recognition includes: iiWAS 2024 best paper award TaDA@VLDB24 best paper award Council for Higher Education (VATAT) scholarship Leonard & Diane Sherman Interdisciplinary Fellowship (2017) Daniel Excellence Scholarship (2019) Miriam & Aaron Gutwirth Memorial Fellowship (2020) He leads NSF-funded research on Human-in-the-loop AI (CRII grant) and co-organizes the HILDA workshop. His teams have won multiple Outstanding MQP Awards for undergraduate research.

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Valeriia Haberlandمشاهده پروفایل

مدرس

Brunel University LondonUnited Kingdom

Dr Valeriia Haberland is a Lecturer in Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences, appointed in 2023. Her expertise bridges computing and biological sciences with a focus on translational health applications. She holds a PhD in Computer Science from King's College London (2015) and was awarded Fellow of the Higher Education Academy (FHEA) in October 2023. Educational background includes advanced training in computational methods and data science. Her research centers on cancer genetics and AI-driven healthcare solutions , specializing in modeling cancer aggressiveness through machine learning analysis of molecular profiles. She pioneers epidemiological causal inference techniques to identify genetically translatable disease factors and develops specialized web infrastructure for biomedical research. Current projects target breast cancer wound monitoring and laminopathy mechanisms. Publication analysis reveals a distinct evolution from early grid computing research (2012-2017) to contemporary health data science leadership. Recent work dominates cancer genomics, proteomics, and osteoarthritis therapeutics using UK Biobank data, with flagship contributions to EpiGraphDB and MR-Base platforms. Her methodology integrates Mendelian randomization, graph databases, and high-throughput sequencing analytics. Dr Haberland secures active research funding including a Brunel Partners-funded project on remote breast cancer wound monitoring (May-July 2024). She supervises MSc dissertations while teaching Modern Data, Logic and Computation, and final-year projects. Her academic service includes placement tutoring and curriculum development in data-intensive healthcare applications.

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Martin Waehlischمشاهده پروفایل

دانشیار

University of BirminghamUnited Kingdom

Martin Waehlisch serves as Associate Professor in the Department of Political Science and International Studies at the University of Birmingham, with dual affiliations at the Birmingham Institute for Sustainability and Climate Action and the Institute for Interdisciplinary Data Science and AI. His work bridges computational methodologies with global governance, focusing on technology's role in transforming peacebuilding and international relations through United Nations frameworks. His research expertise centers on Computational Social Science applications in Peacebuilding (100% fingerprint match), United Nations operations (99%), and Artificial Intelligence integration (67%). Current projects examine how novel data sources, XR technologies, and AI systems enhance diplomatic processes and conflict prevention, with emphasis on Lebanon case studies and UN peace operation modernization. This manifests in his Computational Global Affairs initiative exploring digital twins for societal simulation and AI-driven crisis response systems. Analysis of his 32 research outputs reveals accelerating interdisciplinary convergence since 2020, with 2025 publications demonstrating critical focus on AI ethics in governance, emotion simulation in voice assistants, and data-driven peacebuilding frameworks. His work consistently connects technical innovation to UN Sustainable Development Goals, particularly SDG 16 (Peace, Justice and Strong Institutions). Waehlisch actively hosts and speaks at major academic forums including Smarter Systems for a Sustainable Future (July 2025) and Can AI Fix Government? (May 2025), while maintaining regular media engagement through outlets like AI is supercharging war. Could it also help broker peace? (June 2025). He currently accepts PhD students for Computational Global Affairs research. His institutional collaborations span Weizenbaum Institute, Harvard Dataverse, and UN entities through the Birmingham Institute for Sustainability and Climate Action. Current initiatives include Virtual Worlds Economy development (June 2025) and Simulating Society digital twin projects (June 2025), positioning him at the AI-climate-conflict nexus.

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Ezekiel Fletcherمشاهده پروفایل

استاد مدعو

Michigan State UniversityUnited States

Ezekiel Fletcher serves as an Adjunct Professor at the College of Law, Michigan State University. An enrolled citizen of the Match-e-be-nash-she-wish Band of Pottawatomi Indians (Gun Lake Tribe), he maintains active legal practice across Michigan, Wisconsin, Oklahoma, and tribal jurisdictions with over ten years of experience. Founder of Fletcher Law, PLLC since 2012, he specializes in tribal representation across federal, state, and tribal litigation forums. Education: University of Michigan University of Wisconsin Law School Professor Fletcher's research centers on Federal Indian Law with concentrated expertise in tribal sovereignty challenges, gaming regulation, and economic development frameworks. His work addresses critical intersections between tribal self-governance and external legal systems, particularly regarding land acquisition disputes, sovereign immunity conflicts, and regulatory authority in tribal-state relations. This scholarship directly informs contemporary tribal governance and resource management strategies. Analysis of his publications reveals consistent focus on legal mechanisms for tribal advancement, particularly judicial interpretations affecting labor sovereignty and strategic gaming compact negotiations. These works collectively examine how tribes navigate complex jurisdictional landscapes to achieve economic self-sufficiency while confronting federal preemption and state resistance. Scientific Awards: No awards documented in source material Professional activities indicate no formal student advising relationships, with primary mentorship occurring through law firm operations rather than academic supervision. Grant activities are not referenced in available documentation, though his firm's casework implies sustained project-based funding through client engagements. Professional operations are anchored at Fletcher Law, PLLC, which functions as his primary legal practice entity handling tribal litigation across multiple jurisdictions including federal courts in Michigan, Wisconsin, and Oklahoma, alongside numerous tribal court systems.

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Bidur Khanalمشاهده پروفایل

مدرس ارشد

Kingston UniversityUnited Kingdom

Dr. Bidur Khanal is the current Head of Department for Aerospace and Aircraft Engineering at Kingston University. He joined Kingston in August 2024, bringing extensive experience from MBDA Missile Systems (Team Lead in Simulation and Modelling), Coventry University (Senior Lecturer), and Cranfield University (Lecturer in Complex Weapons). Faculty of Engineering, Computing and the Environment, Kingston University Department of Aerospace and Aircraft Engineering His academic journey includes a PhD in Computational Aerodynamics and Aeroacoustics from Cranfield University (2010), MPhil in Computational Aeroacoustics from the University of Southampton, and a BEng (First Class Honours) in Aeromechanical Systems Engineering from Cranfield University. He also holds a PGCert in Academic Practice. Dr. Khanal's research focuses on high-speed aerodynamics, non-linear heat transfer, and turbomachinery using high-fidelity computational methods. His work spans turbulence modeling, aeroacoustics, and flow control, often addressing complex aerospace engineering challenges related to missiles, turbine blades, and aircraft aerodynamics. His publications highlight expertise in computational fluid dynamics (CFD), unsteady flow analysis, and thermal management. Key contributions include studies on cavity flow control, thrust reverser configurations, and droplet evaporation in multicomponent fuels. Recipient of several national and international awards/scholarships during his time as a student in the UK Chartered Engineer (CEng) Fellow of Royal Aeronautical Society, UK Fellow of the Higher Education Academy, UK Dr. Khanal has delivered lectures as an invited speaker at various forums in the UK and internationally. His teaching encompasses core aerospace topics such as Aerospace Technology Flight Dynamics and Control Aerospace Propulsion Machine Learning and AI for Aerospace Applications . He is passionate about integrating industry insights into academia, having collaborated with entities like Advantage-CFD (Formula One) and Airbus during his academic career. His professional experience includes a postdoctoral role at the Rolls-Royce University Technology Centre at Oxford and a position as Aerodynamic Methods and Tools Engineer at GE Power. Dr. Khanal emphasizes the importance of aligning Kingston University's Aerospace Engineering courses with industry needs, ensuring graduates are equipped with cutting-edge computational techniques and practical knowledge.

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Jiannong Caoمشاهده پروفایل

استاد

The Hong Kong Polytechnic UniversityHong Kong SAR

Jiannong Cao is the Otto Poon Charitable Foundation Professor in Data Science and Chair Professor of Distributed and Mobile Computing at the Department of Computing, The Hong Kong Polytechnic University (PolyU). He currently serves as Dean of the Graduate School and directs multiple research entities including the Research Institute for Artificial Intelligence of Things (RIAIoT), Internet and Mobile Computing Lab (IMCL), and PolyU's University Research Facility in Big Data Analytics (UBDA). Previously, he was the Head of the Department of Computing from 2011 to 2017. Education : BSc from Nanjing University, MSc and PhD from Washington State University Prof. Cao's research focuses on distributed systems , blockchain , wireless sensing , big data analytics , machine learning , mobile cloud , and edge computing . His work includes over 800 publications, 34 patents, and leadership in 130+ research projects with HK$187 million in grants. Recent research trends highlight his contributions to blockchain interoperability , edge AI optimization , secure GPU execution , federated learning frameworks , and IoT-based control systems . These align with his expertise in distributed computing, blockchain, and data-driven intelligent systems. Scientific Awards : PolyU President Award for Excellent Performance/Achievement in Research Ministry of Education Higher Education Outstanding Scientific Research Output Awards – Natural Science (Second Class) China Computer Federation (CCF) Overseas Outstanding Contribution Award IEEE TCCLD Research Innovation Award Silver Medal at Geneva International Exhibition of Inventions Best Paper Awards across multiple IEEE conferences (2011-2021) Prof. Cao has secured over HK$187 million in grants from agencies like NSFC, MOST, RGC, ITC, and industry partners (Alibaba, IBM, Nokia). He has delivered 50+ keynote/invited talks and serves on editorial boards of top journals including IEEE TC, IEEE TPDS, and ACM ToSN. He leads research labs such as RIAIoT and IMCL, focusing on artificial intelligence of things, blockchain integration, and edge computing systems. Current projects include collaborative edge computing, Web3 policies, and secure autonomous cooperation in edge nodes.

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Eduard Dragutمشاهده پروفایل

دانشیار

Temple UniversityUnited States

Eduard Dragut is an Associate Professor in the Department of Computer and Information Science at Temple University's College of Science and Technology. He received his Ph.D. in Computer Science from the University of Illinois at Chicago, M.S. from the University of Iowa, and B.S. in Computer Science from the University of Bucharest. His research spans databases, information retrieval, extraction, cleaning, and integration, with particular focus on opinion mining, web data management, and cyber-infrastructure for science. His educational background includes a Ph.D. from University of Illinois at Chicago, M.S. from University of Iowa, and B.S. from University of Bucharest. His research interests cover multiple areas including databases, information retrieval, extraction, cleaning, and integration; opinion mining and retrieval; web data management; and cyber-infrastructure for science. Current research activities focus on record linkage and fusion, deep learning for entity recognition, sentiment analysis, and news mining. His recent publications demonstrate a consistent focus on information extraction, data integration, and sentiment analysis across various domains including climate science, social media, and scientific literature. His work increasingly bridges traditional database research with machine learning applications, particularly in the areas of entity recognition, knowledge graph construction, and comment analysis. Professional Service Chair SIGMOD/PODS PhD Symposium 2016, San Francisco, USA Chair ICDE PhD Symposium 2015, Chicago, USA PC Member: SIGMOD 2016, SIGMOD 2015, EMNLP 2015, DASFAA 2015, INFOCOM 2015, NAACL-HLT 2015, WWW 2014, COLING 2014, DASFAA 2014, ADBIS 2014 Referee for TKDE, TWEB, TSC, TiiS, ISJ, TKDD, JWS, WWWJ Served on NSF CISE/IIS panels in 2016 Professor Dragut has advised multiple graduate students including Andrew Thomas Schneider (PhD Fourth Year), Lihong He (PhD Second Year), and Abdullah Aljebreen (PhD First Year). His teaching portfolio includes courses such as Data-Intensive and Cloud Computing, Principles of Data Management, and Topics in Computer Science: Information Retrieval. He has taught consistently from Fall 2013 through Spring 2025, demonstrating his commitment to education in the field of computer science. He co-authored the book 'Deep Web Query Interface Understanding and Integration' with Weiyi Meng and Clement Yu, contributing significantly to the field of web data integration.

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Michaela Kümpelمشاهده پروفایل

پژوهشگر

University of BremenGermany

Dr. Michaela Kümpel is a postdoctoral researcher at the Institute of Artificial Intelligence (IAI) at the University of Bremen, working in the Department of Computer Science within the Faculty of Mathematics/Computer Science. She received her PhD (Dr.-Ing.) from the University of Bremen on March 19, 2024, after being a PhD student at the IAI group under Prof. Michael Beetz from 2020 to 2024. She joined the Institute of Artificial Intelligence in 2017 as a research associate and has been actively involved in multiple research projects including Knowledge4Retail, EASE DFG CRC, and H2Media. Her educational background includes a Master of Science in Management Information Systems from the University of South Florida (specializing in statistics and data mining), a Bachelor of Science in Information Systems, and a pre-diploma in business administration from the University of Osnabrueck, Germany. Before her academic career, she completed an apprenticeship as a qualified IT specialist, working at Bankhaus Carl F. Plump and IBM Germany. Dr. Kümpel's research focuses on creating actionable knowledge graphs that bridge Web information with real-world applications for robots and users. Her work spans several key areas: Actionable Knowledge Representation, Flexible action execution on robots, Semantic Digital Twins, Augmented Reality applications, and Machine Learning and Data Mining. She has developed frameworks for creating knowledge graphs from Web information and grounding them in the real world using semantic digital twins to make knowledge actionable for various applications. Her publication record shows a strong progression in robotics and knowledge representation research, with recent work focusing on actionable knowledge graphs for robot manipulation, semantic digital twins for retail environments, and knowledge engineering methodologies for flexible robot manipulation. Her research has particular emphasis on practical applications in retail scenarios, meal preparation tasks, and omni-channel user assistance. Dr. Kümpel actively contributes to the academic community through teaching, having lectured courses like 'Actionable Knowledge Representation' at the University of Bielefeld and Informatica Feminale. She has also received funding for her individual project 'Actionable Knowledge Representation for Robots' (AKR4R) through the Central Research Development Fund of the University of Bremen. She supervises numerous students across bachelor's and master's levels, with thesis topics ranging from robot manipulation to knowledge graph applications. Her work has been presented at major conferences including ICRA, ESWC, FOIS, and AAMAS, demonstrating her active engagement in the robotics and knowledge representation research communities.

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Jiaxin Peiمشاهده پروفایل

استادیار

University of Texas at AustinUnited States

Jiaxin Pei is an incoming Assistant Professor at the School of Information, University of Texas at Austin (starting Fall 2026) and currently a Postdoctoral Fellow at Stanford Institute for Human-Centered Artificial Intelligence (HAI). He holds a PhD from the University of Michigan (2024) and a B.S. from Wuhan University (2019). Current Affiliation: Stanford HAI (Postdoctoral Fellow) Upcoming Affiliation: UT Austin School of Information (Assistant Professor) Alma Maters: University of Michigan (PhD), Wuhan University (B.S.) Research Interests: Human-AI Interaction (interfaces, collaborative agents, evaluation benchmarks) Digital Infrastructure for AI Agents (standards, protocols) Data Pipelines for LLMs (collection, annotation) Collective Intelligence Systems (collaboration, decision-making) Computational Social Science (AI's societal impact) Scientific Awards: Best Student Paper (EAAMO 2025) Best Demo Paper (HCOMP 2024) Honorable Mention (IC2S2 2023) Best Paper (Workshop on Social Influence in Conversations) Advising: Mentoring 12 students (6 current, 6 past) across Stanford, University of Toronto, and University of Michigan. Developing a new Human-Centered AI Systems lab at UT Austin. Key Contributions: Creator of POTATO annotation system. Led WORKBank database development for AI workforce impact analysis.

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Thomas Bonaldمشاهده پروفایل

استاد

Télécom ParisFrance

Thomas Bonald is a Professor at Télécom Paris, Institut Polytechnique de Paris, where he heads the Data, Intelligence and Graphs (DIG) team at the Information Processing and Communication Laboratory (LTCI). His academic career spans several decades with significant contributions to the fields of graph analysis, machine learning, and network systems. Professor Bonald's research interests focus on data analysis and machine learning, particularly for large graphs, text data, and time series. He has made substantial contributions to knowledge base construction, with involvement in the YAGO project, and has developed the scikit-network Python library for graph analysis. His work bridges theoretical foundations with practical applications across various domains including natural language processing, network analysis, and knowledge representation. His recent publication trends reveal a strong emphasis on graph-based machine learning techniques, knowledge graph refinement, and hierarchical classification systems. Bonald's research shows increasing integration of large language models with structured knowledge bases, as evidenced by his work on Wikidata taxonomy refinement and YAGO development. His publications span top-tier venues in machine learning, information retrieval, and network systems. Professor Bonald has mentored numerous PhD students throughout his career, including both current students (Roman Plaud, Yiwen Peng, Rajaa El Hamdani) and former students who have gone on to receive notable awards such as the Telecom Paris thesis award (Céline Comte) and the Gilles Kahn award (Mathieu Feuillet). He has been instrumental in developing educational resources in probability, statistics, graph learning, and reinforcement learning. He leads the DIG research team at LTCI, which focuses on developing advanced methods for analyzing complex graph structures and creating practical tools like scikit-network. His work demonstrates a consistent trajectory from theoretical network analysis to practical implementations in knowledge representation and machine learning systems.

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Krisztian Balogمشاهده پروفایل

استاد

Northeastern UniversityUnited States

Krisztian Balog is a Professor of Computer Science at the University of Stavanger, Norway, and a Staff Research Scientist at Google DeepMind. With a research focus on Interactive AI systems evaluation Conversational information access User modeling and simulation Transparent recommendation systems His recent work emphasizes leveraging large language models for information retrieval tasks and developing simulation frameworks like SimIIR 3. Scientific contributions include receiving the Karen Spärck Jones Award (2018) and Best Resource Paper Award at CIKM’23 . He co-organized the Sim4IA workshop series and leads the NorwAI-funded PhD project on LLM-based recommendation systems.

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